Question

In: Statistics and Probability

5) In the OmniPower Bars scenario, your business objective, to determine the effect that price and...

5) In the OmniPower Bars scenario, your business objective, to determine the effect that price and in-store promotional expenses will have on sales, calls for examining a multiple regression model in which the price of an OmniPower Bar in cents (X1) and the monthly budget for in-store promotional expenses in dollars (X2) are the independent variables and the number of OmniPower Bars sold in a month (Y) is the dependent variable. The data has been collected from a sample of 34 stores in a supermarket chain selected for a test-market study of OmniPower. The data is organized and stored in OmniPower.xlsx file.

(a) State the multiple regression equation.

(b) Interpret the meaning of 1 and 2, in this problem. Significance/Meaning of 1: ____________________________________________________ Significance/Meaning of 2: ____________________________________________________

(c) What is the significance of 0? Significance/Meaning of 0: ____________________________________________________

(d) Predict the mean sales for a store charging $0.69 during a month in which promotion expenses are $300.

Solutions

Expert Solution

(a) The multiple regression equation is:

Y = 5,837.5208 -53.2173*X1 + 3.6131*X2

(b) For every addition in the price of an OmniPower Bar in cents, the number of OmniPower Bars sold in a month will decrease by 53.2173.

For every additional monthly budget for in-store promotional expenses in dollars, the number of OmniPower Bars sold in a month will increase by 3.6131.

(c) There is no meaning of y-intercept because the independent variables cannot be constant.

(d) Y = 5,837.5208 -53.2173*0.69 + 3.6131*300 = 6884.72

0.758
Adjusted R² 0.742
R   0.870
Std. Error   638.065
n   34
k   2
Dep. Var. Sales
ANOVA table
Source SS   df   MS F p-value
Regression 3,94,72,730.7730 2   1,97,36,365.3865 48.48 2.86E-10
Residual 1,26,20,946.6682 31   4,07,127.3119
Total 5,20,93,677.4412 33  
Regression output confidence interval
variables coefficients std. error    t (df=31) p-value 95% lower 95% upper
Intercept 5,837.5208
Price -53.2173 6.8522 -7.766 9.20E-09 -67.1925 -39.2421
Promotion 3.6131 0.6852 5.273 9.82E-06 2.2155 5.0106

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